Update app_v4.py

app_v4.py

@@ -59,7 +59,11 @@ try:
     dump_environment_info()
 except Exception as e:
     print(f"Failed to dump env info: {e}")
-
+    
+def get_image_dimensions(image: Image) -> str:
+    width, height = image.size
+    return f"Original Image Dimensions: {width}x{height}"
+    
 def resize_image_to_max_side(image: Image, max_side_length=1024) -> Image:
     width, height = image.size
     ratio = min(max_side_length / width, max_side_length / height)
@@ -81,7 +85,10 @@ def combine_caption_focus(caption, focus):
 def generate_caption(control_image):
     try:
         if control_image is None:
-            return "Waiting for control image..."
+            return "Waiting for control image...", "Original Image Dimensions: N/A"
+           
+        # Get original dimensions
+        original_dimensions = get_image_dimensions(control_image)
         
         # Resize the image to a maximum longest side of 1024 pixels
         control_image = resize_image_to_max_side(control_image, max_side_length=1024)
@@ -91,17 +98,20 @@ def generate_caption(control_image):
         detailed_caption = mcaption["caption"]
         print(f"Detailed caption: {detailed_caption}")
         
-        return detailed_caption
+        return detailed_caption, original_dimensions
     except Exception as e:
         print(f"Error generating caption: {e}")
-        return "A detailed photograph"
+        return "A detailed photograph", "Original Image Dimensions: N/A"
 
 def generate_focus(control_image, focus_list):
     try:
         if control_image is None:
-            return None
+            return None, "Original Image Dimensions: N/A"
         if focus_list is None:
-            return ""
+            return "", "Original Image Dimensions: N/A"
+        
+        # Get original dimensions
+        original_dimensions = get_image_dimensions(control_image)
         
         # Resize the image to a maximum longest side of 1024 pixels
         control_image = resize_image_to_max_side(control_image, max_side_length=1024)
@@ -110,10 +120,10 @@ def generate_focus(control_image, focus_list):
         focus_query = model.query(control_image, "Please provide a concise but illustrative description of the following area(s) of focus: " + focus_list)
         focus_description = focus_query["answer"]
         print(f"Areas of focus: {focus_description}") 
-        return focus_description
+        return focus_description, original_dimensions
     except Exception as e:
         print(f"Error generating focus: {e}")
-        return "highly detailed photo, raw photography."
+        return "highly detailed photo, raw photography.", "Original Image Dimensions: N/A"
 
 @spaces.GPU(duration=6, progress=gr.Progress(track_tqdm=True))
 @torch.no_grad()
@@ -149,7 +159,7 @@ def generate_image(prompt, scale, steps, control_image, controlnet_conditioning_
 
 def process_image(control_image, user_prompt, system_prompt, scale, steps, 
                 controlnet_conditioning_scale, guidance_scale, seed, 
-                guidance_end, temperature, top_p, max_new_tokens, log_prompt):
+                guidance_end, temperature, max_new_tokens, log_prompt):
     # Initialize with empty caption
     final_prompt = user_prompt.strip()
     # If no user prompt provided, generate a caption first
@@ -210,6 +220,8 @@ with gr.Blocks(title="FLUX Turbo Upscaler", fill_height=True) as demo:
             controlnet_conditioning_scale = gr.Slider(0, 1, value=0.6, label="ControlNet Scale")
             guidance_scale = gr.Slider(1, 30, value=3.5, label="Guidance Scale")
             guidance_end = gr.Slider(0, 1, value=1.0, label="Guidance End")
+            original_dimensions = gr.Markdown(value="Original Image Dimensions: N/A")  # New output for dimensions
+
     with gr.Row():
         with gr.Accordion("Auto-Caption settings", open=False, visible=False):
             system_prompt = gr.Textbox(
@@ -224,11 +236,6 @@ with gr.Blocks(title="FLUX Turbo Upscaler", fill_height=True) as demo:
                 info="Higher values make the output more random, lower values make it more deterministic.",
                 visible=False  # Changed to visible
             )
-            top_p_slider = gr.Slider(
-                minimum=0.0, maximum=1.0, value=0.9, step=0.01,
-                label="Top-p",
-                visible=False  # Changed to visible
-            )
             max_tokens_slider = gr.Slider(
                 minimum=1, maximum=2048, value=368, step=1,
                 label="Max New Tokens",
@@ -238,39 +245,19 @@ with gr.Blocks(title="FLUX Turbo Upscaler", fill_height=True) as demo:
         log_prompt = gr.Checkbox(value=True, label="Log", visible=False)  # Changed to visible
     
     gr.Markdown("**Tips:** 8 steps is all you need! Incredibly powerful tool, usage instructions coming soon.")
-    with gr.Accordion("Help,I keep getting ZeroGPU errors.", open=False, elem_id="zgpu"):
+    with gr.Accordion("Auth for those Getting ZeroGPU errors.", open=False, elem_id="zgpu"):
         msg1 = gr.Markdown()
         try_btn = gr.LoginButton()
-        try:
-            x_ip_token = request.headers['x-ip-token']
-            client = Client("LPX55/zerogpu-experiments", hf_token=huggingface_token, headers={"x-ip-token": x_ip_token})
-            cresult = client.predict(
-                    n=3,
-                    api_name="/predict"
-            )
-            print(f"X TOKEN: {x_ip_token}")
-            print(cresult)
-        except:
-            print("Guess we're just going to have to pretend that Spaces have been broken for almost a year now..")
         
-        # result = client.predict(
-        # 		image=handle_file('https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png'),
-        # 		width=1024,
-        # 		height=1024,
-        # 		overlap_percentage=10,
-        # 		num_inference_steps=8,
-        # 		resize_option="Full",
-        # 		custom_resize_percentage=50,
-        # 		prompt_input="Hello!!",
-        # 		alignment="Middle",
-        # 		overlap_left=True,
-        # 		overlap_right=True,
-        # 		overlap_top=True,
-        # 		overlap_bottom=True,
-        # 		x_offset=0,
-        # 		y_offset=0,
-        # 		api_name="/infer"
+        # sus = ['x-zerogpu-token', 'x-zerogpu-uuid', 'x-proxied-host', 'x-proxied-path', 'x-proxied-replica', 'x-request-id', 'x-ip-token']
+        # x_ip_token = request.headers['X-IP-TOKEN']
+        # print(str(x_ip_token))
+        # client = Client("LPX55/zerogpu-experiments", hf_token=huggingface_token, headers={"x-ip-token": x_ip_token})
+        # cresult = client.predict(
+        #         n=3,
+        #         api_name="/predict"
         # )
+
     caption_state = gr.State()
     focus_state = gr.State()
     log_state = gr.State()
@@ -279,7 +266,7 @@ with gr.Blocks(title="FLUX Turbo Upscaler", fill_height=True) as demo:
         inputs=[
             control_image, prompt, system_prompt, scale, steps, 
             controlnet_conditioning_scale, guidance_scale, seed, 
-            guidance_end, temperature_slider, top_p_slider, max_tokens_slider, log_prompt
+            guidance_end, temperature_slider, max_tokens_slider, log_prompt
         ],
         outputs=[log_state, generated_image, prompt]
     )